mirror of https://github.com/alibaba/EasyCV.git
87 lines
2.5 KiB
Python
87 lines
2.5 KiB
Python
#! -*- coding: utf8 -*-
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# Copyright (c) Alibaba, Inc. and its affiliates.
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import os
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import shutil
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import time
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import unittest
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import uuid
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import torch
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from mmcv.parallel import MMDataParallel
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from tests.ut_config import TMP_DIR_LOCAL
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from easycv.datasets import build_dataloader
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from easycv.file import io
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from easycv.hooks.dino_hook import DINOHook
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from easycv.runner import EVRunner
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from easycv.utils.logger import get_root_logger
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class DummyDataset(object):
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def __getitem__(self, idx):
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output = {'img': [torch.randn(3, 224, 224), torch.randn(3, 224, 224)]}
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return output
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def __len__(self):
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return 4
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def _build_model():
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from easycv.models import build_model
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model = dict(
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type='DINO',
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pretrained=None,
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train_preprocess=[
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'randomGrayScale', 'gaussianBlur', 'solarize'
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], # 2+6 view, has different augment pipeline, dino is complex
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backbone=dict(
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type='PytorchImageModelWrapper',
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# deit(224)
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model_name='dynamic_deit_small_p16',
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),
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# swav need mulit crop ,doesn't support vit based model
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neck=dict(type='DINONeck', in_dim=384, out_dim=65536),
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config=dict(
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use_bn_in_head=False,
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norm_last_layer=True,
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))
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return build_model(model)
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class DINOHookTest(unittest.TestCase):
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def setUp(self):
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print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
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def test_byol_hook(self):
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work_dir = os.path.join(TMP_DIR_LOCAL, uuid.uuid4().hex)
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io.makedirs(work_dir)
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timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
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log_file = os.path.join(work_dir, '{}.log'.format(timestamp))
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logger = get_root_logger(log_file=log_file)
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model = _build_model()
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model = MMDataParallel(model, device_ids=[0]).cuda()
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optimizer = torch.optim.SGD(model.parameters(), lr=0.02, momentum=0.95)
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runner = EVRunner(
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model=model, work_dir=work_dir, optimizer=optimizer, logger=logger)
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dino_hook = DINOHook()
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runner.register_hook(dino_hook)
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dataset = DummyDataset()
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dataloader = build_dataloader(
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dataset, imgs_per_gpu=2, workers_per_gpu=1)
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runner.data_loader = [dataloader]
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runner.run([dataloader], [('train', 1)], 1)
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self.assertEqual(runner.optimizer.param_groups[0]['weight_decay'],
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0.22)
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shutil.rmtree(work_dir, ignore_errors=True)
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if __name__ == '__main__':
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unittest.main()
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